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dc.contributor.authorMattera, Raffaele
dc.contributor.authorGiacalone, Massimiliano
dc.contributor.authorGibert, Karina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2022-01-18T10:18:53Z
dc.date.available2022-01-18T10:18:53Z
dc.date.issued2021-05-28
dc.identifier.citationMattera, R.; Giacalone, M.; Gibert, K. Distribution-based entropy weighting clustering of skewed and heavy tailed time series. "Symmetry-Basel", 28 Maig 2021, vol. 13, núm. 6, p. 959:1-959:27.
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/2117/359885
dc.description.abstractThe goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshOperations research
dc.subject.lcshStochastic analysis
dc.subject.otherClassification
dc.subject.otherGeneralized error distribution
dc.subject.otherSkewness
dc.subject.otherSkewed exponential power distribution
dc.subject.otherFinancial time series
dc.subject.otherPortfolio selection
dc.titleDistribution-based entropy weighting clustering of skewed and heavy tailed time series
dc.typeArticle
dc.subject.lemacInvestigació operativa
dc.subject.lemacAnàlisi estocàstica
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.3390/sym13060959
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90B Operations research and management science
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60G Stochastic processes
dc.relation.publisherversionhttps://www.mdpi.com/2073-8994/13/6/959
dc.rights.accessOpen Access
local.identifier.drac32013335
dc.description.versionPostprint (published version)
local.citation.authorMattera, R.; Giacalone, M.; Gibert, Karina
local.citation.publicationNameSymmetry-Basel
local.citation.volume13
local.citation.number6
local.citation.startingPage959:1
local.citation.endingPage959:27


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